Name
Abstract | ||
|---|---|---|
In many present speech separation approaches, the separation task is formulated as a binary classification problem. Several classification-based approaches have been proposed and performed satisfactorily. However, they do not explicitly model the correlation in time and each time-frequency (T-F) unit is still classified individually. As we know, the speech signal has a very rich time series and temporal dynamic information that can be exploited for speech separation. In this study, we incorporate the correlation in time into classification. Compared with the previous approaches, the proposed approach achieves better separation and generalization performance by using deep stacking networks (DSN) with time series and re-threshold method. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICASSP.2014.6854890 | ICASSP |
Keywords | Field | DocType |
temporal dynamic information,speech recognition,re-threshold method,dsn,deep stacking networks,binary classification problem,speech separation,binary classification,speech signal,correlation theory,correlation,signal classification,computational auditory scene analysis (casa),time-frequency unit,time series,time-frequency analysis,signal to noise ratio,time series analysis,feature extraction,speech,time frequency analysis | Binary classification,Pattern recognition,Computer science,Speech recognition,Correlation,Artificial intelligence,NASA Deep Space Network,Stacking | Conference |
ISSN | Citations | PageRank |
1520-6149 | 6 | 0.50 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuai Nie | 1 | 40 | 8.30 |
| Hui Zhang | 2 | 63 | 22.82 |
| Xueliang Zhang | 3 | 6 | 2.87 |
| Wenju Liu | 4 | 214 | 39.32 |